Stereoscopic warehouse stacked goods checking method based on improved YOLO11-Seg

The improved YOLO11-Seg model conducts a three-dimensional warehouse cargo inventory, which solves the problem of relying on manual and electronic tags in the existing technology, and achieves high-precision and low-cost cargo quantity statistics, which is suitable for edge server deployment.

CN120510488APending Publication Date: 2025-08-19GUANGDONG UNIV OF TECH
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Patent Information

Application Number
CN202510619191.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-14
Publication Date
2025-08-19

AI Technical Summary

Technical Problem

The existing technology has problems in inventory management that rely on manual operations, high costs and low accuracy. Especially in three-dimensional warehouses, existing intelligent inventory methods such as RFID and UAV/AGV combined identification codes still rely on electronic tags, and the limited computing resources of edge servers lead to difficulty in deploying high-precision identification models.

Method used

The improved YOLO11-Seg model is used to conduct a three-dimensional warehouse stacked cargo inventory. By collecting image data sets, labeling processing, building an improved YOLO11-Seg model, and using the training set for model training, combining the convex hull algorithm and area ratio method to calculate the quantity of goods, reducing the dependence on electronic tags and sensors.

Benefits of technology

It realizes high-precision cargo counting in complex warehousing environments, reduces inventory costs, is suitable for edge server deployment, with an average accuracy rate of more than 90%, with small parameters and moderate calculation amount, and is suitable for cargo inventory in three-dimensional warehouses.

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Abstract

The invention provides a stereoscopic warehouse stacked goods checking method based on improved YOLO11-Seg. The stereoscopic warehouse stacked goods checking method comprises the steps of collecting goods images and performing labeling processing on the goods images; the method comprises the following steps: constructing an improved YOLO11-Seg model; carrying out segmentation identification on the cargo image by utilizing the trained improved YOLO11-Seg model; judging whether the condition is legal or not according to the segmentation mask and the mask category information; according to the geometric relationship of the convex hull points, judging whether the cargo top layer is in a full load condition, and if the cargo top layer is in the full load condition, directly calculating the cargo quantity; otherwise, calculating the number of the sub-top-layer boxes according to an estimation algorithm, and then calculating the total number of the cargo boxes. According to the method, the identification of stacked goods is realized by utilizing an improved YOLO11-Seg model, a goods segmentation mask and a mask type are generated, and the total goods quantity is calculated by identifying and distinguishing different layers of goods boxes; the method has the advantages of high generalization, low deployment cost, high checking efficiency and the like.
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